Please wait a minute...
浙江大学学报(工学版)
浙江大学学报(工学版)  2024, Vol. 58 Issue (9): 1781-1789    DOI: 10.3785/j.issn.1008-973X.2024.09.003
计算机与控制工程     
车联网中基于三方Stackelberg博弈的动态多媒体定价方案
张海波1(),王新月1,王冬宇2,刘富3
1. 重庆邮电大学 通信与信息工程学院，重庆 400065
2. 北京邮电大学 人工智能学院，北京 100876
3. 重庆市城市照明中心，重庆 400023
Dynamic multimedia pricing scheme based on three-party Stackelberg game in Internet of vehicles
Haibo ZHANG1(),Xinyue WANG1,Dongyu WANG2,Fu LIU3
1. School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2. School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China
3. Chongqing Urban Lighting Center, Chongqing 400023, China
 全文: PDF(1519 KB)   HTML
摘要：

在当前车联网的应用场景下，中继车辆数据转发的积极性低下与存储空间有限，导致用户体验质量（QoE）降低，为此提出基于三方Stackelberg博弈的动态多媒体定价方案. 为了激励中继车辆参与转发多媒体内容，提出多媒体内容定价框架，其中中继车辆获得全额佣金后向路侧单元（RSU）支付部分佣金. 设计基于Stackelberg博弈的动态定价模型，根据中继车辆、用户车辆与RSU三方的存储空间利用率、内容数据大小和成本因素，建立各自的效用函数，并将其转化为三方四阶段Stackelberg定价模型. 通过反向归纳法证明纳什均衡的存在，实现三方之间的动态定价以得到各自最优策略. 仿真结果表明，所提方案有效解决了中继车辆存储空间过载问题，并提高了中继车辆积极性，且在提升用户QoE方面较传统方案具有优势.
关键词： 车联网(IoV);  动态定价;  Stackelberg博弈;  QoE;  反向归纳法    
Abstract:

The user quality of experience (QoE) is reduced due to the low enthusiasm of the relay vehicle data forwarding and the limited storage space in the current Internet of vehicles (IoV) application scenarios. Thus, a dynamic multimedia pricing scheme based on the three-party Stackelberg game was proposed. Aiming at incentivizing relay vehicles to participate in forwarding multimedia content, a new multimedia content pricing framework was proposed, in which the relay vehicle received a full commission and then paid a partial commission to the roadside unit (RSU). A dynamic pricing model based on Stackelberg game was designed to establish a utility function, which was based on the storage space utilization, the content data size and the cost of the relay vehicle, the user vehicle and the RSU. The utility function was transformed into a three-party, four-stage Stackelberg pricing model. The existence of the Nash equilibrium solution was proved using backward induction technique, and the dynamic pricing process among the three parties was finally realized to achieve their respective optimal strategies. The simulation results showed that the proposed scheme effectively solved the problem of overloaded storage space in the relay vehicle and improved the enthusiasm of the relay vehicle, and it had advantages over the traditional scheme in improving user QoE.
Key words: Internet of vehicles (IoV)    dynamic pricing    Stackelberg game    QoE    backward induction technique
收稿日期: 2023-05-24 出版日期: 2024-08-30
CLC:  TN 92  
基金资助: 国家自然科学基金资助项目（62271094）；长江学者和创新团队发展计划基金资助项目（IRT16R72）；重庆市留创计划创新类资助项目(cx2020059).
作者简介: 张海波（1979—），男，副教授，博士，从事车联网研究. orcid.org/0000-0003-2719-9956. E-mail：zhanghb@cqupt.edu.cn
服务　　
把本文推荐给朋友
加入引用管理器
E-mail Alert
作者相关文章　　
张海波
王新月
王冬宇
刘富

引用本文:

张海波,王新月,王冬宇,刘富. 车联网中基于三方Stackelberg博弈的动态多媒体定价方案[J]. 浙江大学学报(工学版), 2024, 58(9): 1781-1789.

Haibo ZHANG,Xinyue WANG,Dongyu WANG,Fu LIU. Dynamic multimedia pricing scheme based on three-party Stackelberg game in Internet of vehicles. Journal of ZheJiang University (Engineering Science), 2024, 58(9): 1781-1789.

链接本文:

https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2024.09.003    或    https://www.zjujournals.com/eng/CN/Y2024/V58/I9/1781

图 1  IoV通信模型
图 2  三方博弈模型
参数数值参数数值
Pmax/dBm27$\xi $[23]1
$\varepsilon $0.17$\tau $1.6
$\gamma $[21]0.1$\lambda $1/1024
表 1  三方-四阶段Stackelberg博弈迭代算法的仿真参数表
图 3  佣金比例变化对中继车辆效用与RSU效用的影响
图 4  不同传输速率对用户效用的影响
图 5  不同空间利用率下中继车辆效用
图 6  不同方案的RSU效用对比
图 7  不同方案的中继车辆效用对比
图 8  不同方案的用户车辆效用对比
1 VOROBYEV A I, KOVESHNIKOV A A, GAVRILYUK M V, et al. Classification of integration platforms of intelligent transport systems [C]// 2023 Systems of Signals Generating and Processing in the Field of on Board Communications . Moscow: IEEE, 2023: 1−5.
2 WANG J, ZHU K, HOSSAIN E Green Internet of vehicles (IoV) in the 6G era: toward sustainable vehicular communications and networking[J]. IEEE Transactions on Green Communications and Networking, 2022, 6 (1): 391- 423
doi: 10.1109/TGCN.2021.3127923
3 XU W, GUO S, MA S, et al Augmenting drive-thru internet via reinforcement learning-based rate adaptation[J]. IEEE Internet of Things Journal, 2020, 7 (4): 3114- 3123
doi: 10.1109/JIOT.2020.2965148
4 ZHANG H, XU R, LI Z, et al Resource-aware video delivery in fog radio access networks: a joint QoE and QoS perspective[J]. IEEE Transactions on Vehicular Technology, 2023, 72 (5): 6669- 6682
doi: 10.1109/TVT.2023.3234141
5 叶进, 肖庆宇, 陈梓晗, 等 以用户QoE预测值为奖励的视频自适应比特率算法[J]. 电子科技大学学报, 2021, 50 (2): 236- 242
YE Jin, XIAO Qingyu, CHEN Zihan, et al A video adaptive bitrate algorithm with user QoE prediction as reward[J]. Journal of University of Electronic Science and Technology of China, 2021, 50 (2): 236- 242
doi: 10.12178/1001-0548.2020325
6 MUSTAFA R U, MOURA D, ROTHENBERG C E. Machine learning approach to estimate video QoE of encrypted dash traffic in 5G networks [C]// 2021 IEEE Statistical Signal Processing Workshop . Brazil: IEEE, 2021: 586−589.
7 ROTHENBERG C E, PEREZ D A L, SOUSA N F S, et al. Intent-based control loop for dash video service assurance using ML-based edge QoE estimation [C]// 2020 6th IEEE Conference on Network Softwarization . Belgium: IEEE, 2020: 353−355.
8 AMOUR L, MUSHTAQ M S, SOUIHI S, et al. QoE-based framework to optimize user perceived video quality [C]// 2017 IEEE 42nd Conference on Local Computer Networks . Singapore: IEEE, 2017: 599−602.
9 ZHANG H, LU Z M, WEN X M, et al QoE-based reduction of handover delay for multimedia application in IEEE 802.11 networks[J]. IEEE Communications Letters, 2015, 19 (11): 1873- 1876
doi: 10.1109/LCOMM.2015.2459048
10 LI C, TONI L, ZOU J H, et al QoE-driven mobile edge caching placement for adaptive video streaming[J]. IEEE Transactions on Multimedia, 2018, 20 (4): 965- 984
doi: 10.1109/TMM.2017.2757761
11 BAEK B, LEE J, PENG Y, et al Three dynamic pricing schemes for resource allocation of edge computing for IoT environment[J]. IEEE Internet of Things Journal, 2020, 7 (5): 4292- 4303
doi: 10.1109/JIOT.2020.2966627
12 MITRA D, SRIDHAR A. Consortiums of ISP-content providers formed by nash bargaining for Internet content delivery [C]// IEEE Conference on Computer Communications . Paris: IEEE, 2019: 631−639.
13 RAMAMOORYHY K M K, WANG W. QoE-sensitive economic pricing model for wireless multimedia communications using Stackelberg game [C]// IEEE Global Communications Conference . Waikoloa: IEEE, 2019: 1−6.
14 RAMAMOORYHY K M K. User preference aware multimedia pricing model using game theory and prospect theory for wireless communications [C]// 34th IEEE/ACM International Conference on Automated Software Engineering . San Diego: IEEE, 2019: 1265−1267.
15 DENG G, LI F C, WANG L W. Cooperative downloading in VANETs-LTE heterogeneous network based on named data [C]// IEEE Conference on Computer Communications Workshops . San Francisco: IEEE, 2016: 233−238.
16 CHAI R, LV Y, YANG B, et al. Cooperative game based relay vehicle selection algorithm for VANETs [C]// 14th International Symposium on Communications and Information Technologies . Incheon: IEEE, 2014: 30−34.
17 MA X, WANG L. Game theory based cooperation incentive mechanism in vehicular ad hoc networks [C]// International Conference on Management of e-Commerce and e-Government . Beijing: IEEE, 2012: 127−132.
18 HUI Y, SU Z, LUAN T H, et al Reservation service: trusted relay selection for edge computing services in vehicular networks[J]. IEEE Journal on Selected Areas in Communications, 2020, 38 (12): 2734- 2746
doi: 10.1109/JSAC.2020.3005468
19 XIONG Z G, XIAO N, XU F, et al An equivalent exchange based data forwarding incentive scheme for socially aware networks[J]. Journal of Signal Processing Systems, 2021, 93: 249- 263
doi: 10.1007/s11265-020-01610-6
20 XIA Z, MAO X, GU K, et al Two-dimensional behavior-marker-based data forwarding incentive scheme for fog-computing-based SIoVs[J]. IEEE Transactions on Computational Social Systems, 2022, 9 (5): 1406- 1418
doi: 10.1109/TCSS.2021.3129898
21 RAMAMOORYHY K M K, WANG W A QoE-driven pricing scheme for inter-vehicular communications with four-stage Stackelberg game[J]. IEEE Transactions on Vehicular Technology, 2022, 71 (3): 3121- 3130
doi: 10.1109/TVT.2021.3138328
22 SENNAN S, RAMASUBBAREDDY S, BALAASUBR- AMANIVAM S, et al MADCR: mobility aware dynamic clustering-based routing protocol in Internet of Vehicles[J]. China Communications, 2021, 18 (7): 69- 85
doi: 10.23919/JCC.2021.07.007
[1] 张海波,刘子琪,刘开健,徐勇军. 活跃度感知的社交车辆分簇算法[J]. 浙江大学学报(工学版), 2022, 56(5): 1044-1054.
[2] 刘雪娇,殷一丹,陈蔚,夏莹杰,许佳丽,韩立东. 基于区块链的车联网数据安全共享方案[J]. 浙江大学学报(工学版), 2021, 55(5): 957-965.
[3] 周健,张玉华. 传统零售商渠道选择策略微分博弈模型[J]. 浙江大学学报(工学版), 2019, 53(9): 1720-1727.